KEYWORDS: Signal to noise ratio, Digital filtering, Algorithm development, Electronic filtering, Interference (communication), System identification, Detection and tracking algorithms, Computer simulations, Control systems, Error analysis
The recursive least-squares (RLS) is a very popular adaptive algorithm, which is widely used in many system identification problems. The performance of the algorithm is controlled by two important parameters, i.e., the forgetting factor and the regularization parameter. The forgetting factor controls the “memory” of the algorithm and its value leads to a compromise between low misadjustment and fast convergence. The regularization term is required in most adaptive algorithms and its role becomes very critical in the presence of additive noise. In this paper, we present the regularized RLS algorithm and we develop a method to find its regularization parameter, which is related to the signal-to-noise ratio (SNR). Also, using a proper estimation of the SNR, we present a variable-regularized RLS (VR-RLS) algorithm.
KEYWORDS: Detection and tracking algorithms, Digital filtering, Interference (communication), System identification, Signal to noise ratio, Filtering (signal processing), Algorithm development, Electronic filtering, Signal processing, Optoelectronics
The recursive least-squares (RLS) is a very popular adaptive algorithm, which is widely used in many system identification problems. The parameter that crucially influences the performance of the RLS algorithm is the forgetting factor. The value of this parameter leads to a compromise between tracking, misadjustment, and stability. In this paper, we present some insights on the performance of variable forgetting factor RLS (VFF-RLS) algorithms, in the context of system identification. Besides the classical RLS algorithm, we mainly focus on two recently proposed VFF-RLS algorithms. The novelty of the experimental setup is that we use real-world signals provided by Romanian Air Traffic Services Administration, i.e., voice and noise signals corresponding to real communication channels. In this context, the Air Traffic Control (ATC) communication represents a challenging task, usually involving non-stationary environments and stability issues.
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